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Revealing Persistent Structure of International Trade by Nonnegative Matrix Factorization

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Complex Networks & Their Applications VI (COMPLEX NETWORKS 2017)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 689))

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Abstract

The international trade network is analyzed to identify latent relations between nations and traded commodities. Nonnegative matrix factorization (NMF) is used to decompose the international trade network from 2000 to 2010 into groups of nations and commodities. Groups in consecutive years are compared, and it is shown that there are persistent characteristics within these groups. We identify economically interpretable results, such as intra-regional trade, accounting for the largest component and the recent growth of Chinese exports of commodities.

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References

  1. https://unstats.un.org/unsd/tradekb/Knowledgebase/50262/Search-SITC-code-description

  2. http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.NMF.html was used for this paper

  3. Barigozzi, M., Fagiolo, G., Garlaschelli, D.: Multinetwork of international trade: A commodity-specific analysis. Phys. Rev. E 81(4), 046104 (2010)

    Article  Google Scholar 

  4. Barigozzi, M., Fagiolo, G., Mangioni, G.: Community structure in the multi-network of international trade. In: Complex Networks, pp. 163–175. Springer (2011)

    Google Scholar 

  5. Gligorijevic, V., Panagakis, Y., Zafeiriou, S.: Non-negative matrix factorizations for multiplex network analysis (2016). arXiv:1612.00750

  6. Hoyer, P.O.: Non-negative matrix factorization with sparseness constraints. J. Mach. Learn. Res. 5, 1457–1469 (2004)

    Google Scholar 

  7. Hsieh, C.J., Dhillon, I.S.: Fast coordinate descent methods with variable selection for non-negative matrix factorization. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1064–1072. ACM (2011)

    Google Scholar 

  8. Ikeda, Y., Iyetomi, H., Mizuno, T., Ohnishi, T., Watanabe, T.: Community structure and dynamics of the industry sector-specific international-trade-network. In: Signal-Image Technology and Internet-Based Systems (SITIS), 2014 Tenth International Conference on, pp. 456–461. IEEE (2014)

    Google Scholar 

  9. Ikeda, Y., Watanabe, T.: Who buys what, where: Reconstruction of the international trade flows by commodity and industry. In: H. Cherifi, S. Gaito, W. Quattrociocchi, A. Sala (eds.) Complex Networks and Their Applications V: Proceedings of the 5th International Workshop on Complex Networks and their Applications (COMPLEX NETWORKS 2016), pp. 657–670. Springer International Publishing, Cham (2017)

    Google Scholar 

  10. Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788 (1999)

    Article  MATH  Google Scholar 

  11. Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: Advances in Neural Information Processing Systems, pp. 556–562 (2001)

    Google Scholar 

  12. Mizuno, T., Ohnishi, T., Watanabe, T.: Structure of global buyer-supplier networks and its implications for conflict minerals regulations. EPJ Data Sci. 5(1), 2 (2016)

    Article  Google Scholar 

  13. Simoes, A.J.G., Hidalgo, C.A.: The economic complexity observatory: An analytical tool for understanding the dynamics of economic development. In: Workshops at the Twenty-Fifth AAAI Conference on Artificial Intelligence (2011)

    Google Scholar 

  14. Vidmer, A., Zeng, A., Medo, M., Zhang, Y.C.: Prediction in complex systems: The case of the international trade network. Physica A: Stat. Mech. Appl. 436, 188–199 (2015)

    Article  Google Scholar 

  15. Wang, F., Li, T., Wang, X., Zhu, S., Ding, C.: Community discovery using nonnegative matrix factorization. Data Min. Knowl. Discov. 22(3), 493–521 (2011)

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

This work was supported by JSPS KAKENHI Grant Number 16H02872.

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Authors and Affiliations

  1. Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan

    Chikara Mizokami & Takaaki Ohnishi

Authors
  1. Chikara Mizokami
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  2. Takaaki Ohnishi
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Corresponding author

Correspondence to Chikara Mizokami .

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Editors and Affiliations

  1. University of Lyon 2, Lyon, France

    Chantal Cherifi

  2. University of Burgundy, Dijon, France

    Hocine Cherifi

  3. École Normale Supérieure de Lyon, Lyon, France

    Márton Karsai

  4. University College London, London, United Kingdom

    Mirco Musolesi

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Mizokami, C., Ohnishi, T. (2018). Revealing Persistent Structure of International Trade by Nonnegative Matrix Factorization. In: Cherifi, C., Cherifi, H., Karsai, M., Musolesi, M. (eds) Complex Networks & Their Applications VI. COMPLEX NETWORKS 2017. Studies in Computational Intelligence, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-72150-7_88

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  • DOI: https://doi.org/10.1007/978-3-319-72150-7_88

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72149-1

  • Online ISBN: 978-3-319-72150-7

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